In the field of computer vision, image segmentation functions are utilized to identify and segment target objects in images. Image segmentation can be useful in a variety of contexts and applications. For example, medical image segmentation is an important aspect of medical image analysis. Accurately performing segmentation functions on medical images can provide significant insights into early manifestations of life-threatening diseases, and can assist medical practitioners with diagnosing patients and grading severities of diseases. Image segmentation is also useful in many other contexts and applications including intelligent surveillance systems, facial recognitions systems, etc.
Performing automated image segmentation using computer vision functions is a very complex and challenging task. To accurately perform automated image segmentation, the computer vision applications must account for a variety of technical problems. One technical problem relates to configuring and training a neural network architecture in an appropriate manner to enable identification of target object boundaries with high accuracy and precision. This is especially important in the context of medical images, given that the accuracy and precision of the segmentation results may affect patients' diagnoses and/or treatments. In medical image segmentation, different target objects can have similar appearances, thus making it difficult to accurately identify and segment the target objects. Additional complexities can arise in scenarios in which segmentation functions are performed on images that include inconspicuous objects that are overshadowed by irrelevant salient objects, which can cause neural network architectures to make false predictions.